The following relates generally to the image processing arts, image segmentation arts, and related arts, and to applications employing segmented images such as urology treatment planning, inverse planning for intensity-modulated radiation therapy (IMRT), and so forth.
In various imaging tasks such as urology treatment planning, radiation therapy planning, and so forth, the prostate or other organ or tumor of interest is segmented in a computed tomography (CT), magnetic resonance (MR), ultrasound (US), or other 2D or 3D medical image. The segmentation process entails delineating boundaries of the prostate, tumor, or other anatomical feature(s) of interest in the image. Various approaches may be used to perform the segmentation, such as an adaptive mesh fitting approach or a region growing approach. Most automated segmentation approaches are iterative in nature.
A problem in such automated segmentation approaches is that sometimes the segmentation algorithm fails to converge to the correct solution, e.g. the mesh may be erroneously fitted to something other than the organ of interest, or the growing region may leak out of a gap in the region boundary. Conventionally, the solution is to have a radiologist or other trained professional review the segmentation result for accuracy, and, if an inaccurate result is obtained, the radiologist takes suitable remedial action.
Segmentation failures are reduced, but have not been eliminated, by training the segmentation algorithm on a large set of training images. The training set should encompass the range of image variations likely to be encountered, but complete coverage of all possible variants is generally not possible. Moreover, robustness of the segmentation process depends on the initial conditions (e.g. initial mesh, or seed locations for region growth approaches).
The present disclosure provides approaches for addressing this problem and others.